The report is based on a global survey of over 3,000 executives, managers and analysts. That informs their assessment of the huge difference between companies characterized as AI leaders and the ones that have yet to get on board.

About 75 percent of executives do buy into the potential for AI to advance their business in new ways and nearly 85 percent believe it will open a competitive advantage. Also, 63 percent of the companies anticipate seeing a major impact from AI within the next five years.

But there's a gap between that belief and implementation. Only about 20 percent of companies have taken the step of employing AI in any process, and a mere 5 percent can claim to have "extensively incorporated AI." That fits with the much lower percentage of companies who say they see a significant impact from AI at present - just 14 percent.

Less than 40 percent of the companies represented have any AI plan set. Those that do tend to be larger enterprises that employ upwards of 100,000 people, though even among those, only 50 percent have the strategy.

Four different groups

The research reveals four "maturity clusters" it names Pioneers, Investigators, Experimenters and Passives. The report identifies their level of representations and their level of understanding and/or adoption of AI as follows:

Pioneers (19%): Organizations that both understand and have adopted AI. These organizations are on the leading edge of incorporating AI into their organization's offerings and internal processes.

Investigators (32%): Organizations that understand AI but are not deploying it beyond the pilot stage. Their investigation into what AI may offer emphasizes looking before leaping.

Experimenters (13%): Organizations that are piloting or adopting AI without deep understanding. These organizations are learning by doing.

Passives (36%): Organizations with no adoption or much understanding of AI.

A primary differentiator, the report points out, for those who lead in this area is that they "not only have a much deeper appreciation about what's required to produce AI than laggards, they are also more likely to have senior leadership support and have developed a business case for AI initiatives."

One case in point is the experience of Airbus. Matthew Evans, vice president of digital transformation at the company, said that applying AI to finding previously applied solutions to address problems in real time cut back more than 33 percent of the time lost to such events.

However, Evans made it clear that the AI initiative was viewed as a solution to a particular business problem rather than as a venture into new technology. He explained it this way:

"Well, strictly speaking, we don't invest in AI. We don't invest in natural language processing. We don't invest in image analytics. We're always investing in a business problem."

While some in a particular industry are really gung-ho on applying AI, others hold back. The report points to Ping An, a Chinese insurance company, as a leader that has set up 30 AI initiatives worked on by the 110 data scientists on staff. On the other hand, an unnamed executive at a "large Western insurer" admits that their ventures don't go beyond "'experimenting with chatbots.'"

That insurance company may be classified among the Experimenters. Of that group, 32 percent comprehend the impact AI can have on "business value." That is better than the 23 percent of the Passives but far behind the 92 percent of the Pioneers and the 90 percent of Investigators.

The foundation of AI

The differences between the two ends of the groups become much more pronounced with respect to the appreciation of how to use the algorithms effectively. "Pioneers are 12 times more likely to understand the process for training algorithms, 10 times more likely to understand the development costs of AI-based products and services, and 8 times more likely to understand the data that's needed for training AI algorithms" than Passives.

Some business people simply fail to comprehend the need for a strong data base. Jacob Spoelstra, director of data science at Microsoft, comments on that:

"A mistake we often see is that organizations don't have the historical data required for the algorithms to extract patterns for robust predictions. For example, they'll bring us in to build a predictive maintenance solution for them, and then we'll find out that there are very few, if any, recorded failures. They expect AI to predict when there will be a failure, even though there are no examples to learn from."

The way forward

In addition to getting the data and the people with the machine learning skills to set up the AI projects, for businesses that seek to realize its potential, there are other things on that executives should add to their to-do list. They include expanding their knowledge of AI, extending "their perspective on how to organize their business around AI," and building a more comprehensive picture of their own and their competitors' position.

Essentially, the report recommends businesses bear in mind what the American astronaut Jim Lovell observed: "There are people who make things happen, there are people who watch things happen, and there are people who wonder what happened." Those who prepare to apply AI to their businesses are the ones who will make things happen, a prerequisite for success.